Nicholas J. Robert

20.9k total citations · 4 hit papers
181 papers, 10.0k citations indexed

About

Nicholas J. Robert is a scholar working on Oncology, Cancer Research and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Nicholas J. Robert has authored 181 papers receiving a total of 10.0k indexed citations (citations by other indexed papers that have themselves been cited), including 112 papers in Oncology, 87 papers in Cancer Research and 58 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Nicholas J. Robert's work include Breast Cancer Treatment Studies (62 papers), Cancer Treatment and Pharmacology (61 papers) and HER2/EGFR in Cancer Research (43 papers). Nicholas J. Robert is often cited by papers focused on Breast Cancer Treatment Studies (62 papers), Cancer Treatment and Pharmacology (61 papers) and HER2/EGFR in Cancer Research (43 papers). Nicholas J. Robert collaborates with scholars based in United States, Canada and France. Nicholas J. Robert's co-authors include Dennis J. Slamon, Melody Cobleigh, Charles L. Vogel, Debu Tripathy, Louis Fehrenbacher, Steven Shak, Virginia Paton, Suzy Scholl, Janet Wolter and Edith A. Perez and has published in prestigious journals such as New England Journal of Medicine, Journal of Clinical Oncology and SHILAP Revista de lepidopterología.

In The Last Decade

Nicholas J. Robert

170 papers receiving 9.7k citations

Hit Papers

Multinational Study of th... 1999 2026 2008 2017 1999 2005 2011 2016 500 1000 1.5k 2.0k

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Nicholas J. Robert 6.7k 4.1k 2.2k 2.0k 2.0k 181 10.0k
Elżbieta Senkus 6.2k 0.9× 4.0k 1.0× 1.8k 0.8× 2.3k 1.2× 2.5k 1.3× 113 10.1k
Silvana Martino 9.2k 1.4× 6.6k 1.6× 3.1k 1.4× 2.0k 1.0× 2.2k 1.1× 125 12.9k
Robert Paridaens 6.3k 0.9× 3.6k 0.9× 3.3k 1.5× 1.8k 0.9× 2.1k 1.1× 307 9.9k
Elizabeth Tan-Chiu 5.2k 0.8× 3.2k 0.8× 3.2k 1.4× 1.5k 0.7× 1.1k 0.5× 48 8.7k
Karen N. Price 5.8k 0.9× 4.9k 1.2× 2.6k 1.2× 1.4k 0.7× 1.9k 1.0× 103 9.1k
Semiglazov Vf 8.5k 1.3× 5.6k 1.4× 1.2k 0.5× 2.1k 1.0× 2.1k 1.1× 239 11.9k
Stephen Chia 7.4k 1.1× 5.9k 1.5× 1.4k 0.6× 3.2k 1.6× 3.0k 1.5× 234 11.5k
Grazia Arpino 4.2k 0.6× 2.8k 0.7× 1.6k 0.7× 1.7k 0.9× 1.6k 0.8× 157 6.7k
Jacques Bonneterre 5.1k 0.8× 2.8k 0.7× 1.5k 0.7× 1.9k 1.0× 1.7k 0.8× 257 8.0k
L. Mauriac 8.0k 1.2× 5.5k 1.4× 4.0k 1.8× 1.5k 0.7× 2.6k 1.3× 171 11.5k

Countries citing papers authored by Nicholas J. Robert

Since Specialization
Citations

This map shows the geographic impact of Nicholas J. Robert's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Nicholas J. Robert with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nicholas J. Robert more than expected).

Fields of papers citing papers by Nicholas J. Robert

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nicholas J. Robert. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Nicholas J. Robert. The network helps show where Nicholas J. Robert may publish in the future.

Co-authorship network of co-authors of Nicholas J. Robert

This figure shows the co-authorship network connecting the top 25 collaborators of Nicholas J. Robert. A scholar is included among the top collaborators of Nicholas J. Robert based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Nicholas J. Robert. Nicholas J. Robert is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Butrynski, James E., John C. Paschold, Patrick J. Ward, et al.. (2024). Biomarker testing in early-stage NSCLC: Results from the MYLUNG Consortium.. Journal of Clinical Oncology. 42(16_suppl). 8047–8047. 1 indexed citations
2.
Robert, Nicholas J., Connie Chen, Sindy Kim, et al.. (2024). Real-world comparative effectiveness of palbociclib plus aromatase inhibitor in HR+/HER2- metastatic breast cancer. Future Oncology. 20(12). 761–780. 1 indexed citations
3.
Antwi, Henry Asante, et al.. (2024). Health equity principles for oncology real world evidence studies. The Oncologist. 29(10). e1260–e1271.
5.
Goldschmidt, Jerome H., Philip K. Chan, Liwei Chen, et al.. (2023). Real‐world outcomes of 18,186 metastatic solid tumor outpatients: Baseline blood cell counts correlate with survival after immune checkpoint inhibitor therapy. Cancer Medicine. 12(22). 20783–20797. 14 indexed citations
6.
Wilson, Thomas W., et al.. (2023). Feasibility of Using Oncology-Specific Electronic Health Record (EHR) Data to Emulate Clinical Trial Eligibility Criteria. SHILAP Revista de lepidopterología. 2(2). 140–147.
8.
Cosgrove, David, et al.. (2023). Comparison of real-world mortality data among patients with glioblastoma (GBM) and metastatic pancreatic cancer (mPC) treated in the community oncology setting.. Journal of Clinical Oncology. 41(16_suppl). e18805–e18805. 1 indexed citations
9.
Young, Jessica C., et al.. (2022). Oncology Drug Effectiveness from Electronic Health Record Data Calibrated Against RCT Evidence: The PARSIFAL Trial Emulation. Clinical Epidemiology. Volume 14. 1135–1144. 5 indexed citations
10.
Samlowski, Wolfram E., Nicholas J. Robert, Liwei Chen, et al.. (2022). Real‐World nivolumab dosing patterns and safety outcomes in patients receiving adjuvant therapy for melanoma. Cancer Medicine. 12(3). 2378–2388. 3 indexed citations
11.
Wilfong, Lalan S., et al.. (2021). Practice patterns among oncologists participating in the oncology care model after three years. Journal of Cancer Policy. 29. 100294–100294. 1 indexed citations
12.
13.
Stern, Howard M., Humphrey Gardner, Tomasz Burzykowski, et al.. (2015). PTEN Loss Is Associated with Worse Outcome in HER2 -Amplified Breast Cancer Patients but Is Not Associated with Trastuzumab Resistance. Clinical Cancer Research. 21(9). 2065–2074. 57 indexed citations
15.
Paul, Devchand, Scot Sedlacek, Anne Favret, et al.. (2013). Adjuvant docetaxel and cyclophosphamide plus trastuzumab in patients with HER2-amplified early stage breast cancer: a single-group, open-label, phase 2 study. The Lancet Oncology. 14(11). 1121–1128. 92 indexed citations
16.
Robert, Nicholas J. & Anne Favret. (2007). HER2-Positive Advanced Breast Cancer. Hematology/Oncology Clinics of North America. 21(2). 293–302. 7 indexed citations
17.
Robert, Nicholas J., Brian Leyland‐Jones, Lina Asmar, et al.. (2006). Randomized Phase III Study of Trastuzumab, Paclitaxel, and Carboplatin Compared With Trastuzumab and Paclitaxel in Women With HER-2–Overexpressing Metastatic Breast Cancer. Journal of Clinical Oncology. 24(18). 2786–2792. 342 indexed citations
18.
Page, David L., Robert P. Gray, D. Craig Allred, et al.. (2001). Prediction of Node-Negative Breast Cancer Outcome by Histologic Grading and S-Phase Analysis by Flow Cytometry. American Journal of Clinical Oncology. 24(1). 10–18. 43 indexed citations
19.
Saphner, Thomas J., Edie Weller, Douglass C. Tormey, et al.. (2000). 21-Day Oral Etoposide for Metastatic Breast Cancer. American Journal of Clinical Oncology. 23(3). 258–262. 19 indexed citations
20.
Cobleigh, Melody, Charles L. Vogel, Debu Tripathy, et al.. (1999). Multinational Study of the Efficacy and Safety of Humanized Anti-HER2 Monoclonal Antibody in Women Who Have HER2-Overexpressing Metastatic Breast Cancer That Has Progressed After Chemotherapy for Metastatic Disease. Journal of Clinical Oncology. 17(9). 2639–2639. 2174 indexed citations breakdown →

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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